Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations500
Missing cells1946
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory672.3 KiB
Average record size in memory1.3 KiB

Variable types

Text4
Categorical19
Numeric4
DateTime2

Alerts

capacity_kg is highly overall correlated with product_categoryHigh correlation
capacity_liters is highly overall correlated with product_category and 1 other fieldsHigh correlation
capacity_place_settings is highly overall correlated with product_category and 1 other fieldsHigh correlation
capacity_tons is highly overall correlated with product_categoryHigh correlation
feature_1 is highly overall correlated with product_category and 2 other fieldsHigh correlation
product_category is highly overall correlated with capacity_kg and 6 other fieldsHigh correlation
sub_type is highly overall correlated with feature_1 and 2 other fieldsHigh correlation
technology is highly overall correlated with capacity_liters and 4 other fieldsHigh correlation
warranty_duration_months is highly overall correlated with warranty_yearsHigh correlation
warranty_years is highly overall correlated with warranty_duration_monthsHigh correlation
product_id has 15 (3.0%) missing values Missing
product_category has 15 (3.0%) missing values Missing
sub_type has 15 (3.0%) missing values Missing
model_name has 15 (3.0%) missing values Missing
capacity_tons has 423 (84.6%) missing values Missing
capacity_liters has 247 (49.4%) missing values Missing
capacity_kg has 423 (84.6%) missing values Missing
capacity_place_settings has 418 (83.6%) missing values Missing
technology has 15 (3.0%) missing values Missing
feature_1 has 15 (3.0%) missing values Missing
energy_rating_stars has 90 (18.0%) missing values Missing
color has 15 (3.0%) missing values Missing
price_inr has 15 (3.0%) missing values Missing
manufacturing_date has 15 (3.0%) missing values Missing
warranty_years has 15 (3.0%) missing values Missing
customer_rating has 15 (3.0%) missing values Missing
city has 15 (3.0%) missing values Missing
platform has 15 (3.0%) missing values Missing
discount_offered has 15 (3.0%) missing values Missing
availability has 15 (3.0%) missing values Missing
warranty_duration_months has 15 (3.0%) missing values Missing
review_sentiment has 15 (3.0%) missing values Missing
return_status has 15 (3.0%) missing values Missing
complaint_text has 15 (3.0%) missing values Missing
resolved_status has 15 (3.0%) missing values Missing
review_date has 15 (3.0%) missing values Missing
reviewer_location has 15 (3.0%) missing values Missing
product_name has 15 (3.0%) missing values Missing
discount_offered has 63 (12.6%) zeros Zeros

Reproduction

Analysis started2025-08-19 01:02:56.233877
Analysis finished2025-08-19 01:03:02.286484
Duration6.05 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

product_id
Text

Missing 

Distinct484
Distinct (%)99.8%
Missing15
Missing (%)3.0%
Memory size31.9 KiB
2025-08-19T01:03:02.555068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters4365
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)99.6%

Sample

1st rowPID433097
2nd rowPID251899
3rd rowPID857398
4th rowPID934528
5th rowPID952837
ValueCountFrequency (%)
pid514014 2
 
0.4%
pid947406 1
 
0.2%
pid853697 1
 
0.2%
pid587371 1
 
0.2%
pid552702 1
 
0.2%
pid937912 1
 
0.2%
pid952837 1
 
0.2%
pid651778 1
 
0.2%
pid796565 1
 
0.2%
pid623558 1
 
0.2%
Other values (474) 474
97.7%
2025-08-19T01:03:02.964405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 485
11.1%
I 485
11.1%
D 485
11.1%
3 327
7.5%
2 309
 
7.1%
4 300
 
6.9%
6 299
 
6.8%
8 295
 
6.8%
9 288
 
6.6%
5 286
 
6.6%
Other values (3) 806
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 485
11.1%
I 485
11.1%
D 485
11.1%
3 327
7.5%
2 309
 
7.1%
4 300
 
6.9%
6 299
 
6.8%
8 295
 
6.8%
9 288
 
6.6%
5 286
 
6.6%
Other values (3) 806
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 485
11.1%
I 485
11.1%
D 485
11.1%
3 327
7.5%
2 309
 
7.1%
4 300
 
6.9%
6 299
 
6.8%
8 295
 
6.8%
9 288
 
6.6%
5 286
 
6.6%
Other values (3) 806
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 485
11.1%
I 485
11.1%
D 485
11.1%
3 327
7.5%
2 309
 
7.1%
4 300
 
6.9%
6 299
 
6.8%
8 295
 
6.8%
9 288
 
6.6%
5 286
 
6.6%
Other values (3) 806
18.5%

product_category
Categorical

High correlation  Missing 

Distinct6
Distinct (%)1.2%
Missing15
Missing (%)3.0%
Memory size34.1 KiB
Refrigerator
88 
Dishwasher
83 
Water Dispenser
82 
Air Cooler
78 
Washing Machine
78 

Length

Max length15
Median length12
Mean length12.795876
Min length10

Characters and Unicode

Total characters6206
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDishwasher
2nd rowWashing Machine
3rd rowAir Conditioner
4th rowAir Conditioner
5th rowAir Conditioner

Common Values

ValueCountFrequency (%)
Refrigerator 88
17.6%
Dishwasher 83
16.6%
Water Dispenser 82
16.4%
Air Cooler 78
15.6%
Washing Machine 78
15.6%
Air Conditioner 76
15.2%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:03.083998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:03.185081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
air 154
19.3%
refrigerator 88
11.0%
dishwasher 83
10.4%
water 82
10.3%
dispenser 82
10.3%
cooler 78
9.8%
washing 78
9.8%
machine 78
9.8%
conditioner 76
9.5%

Most occurring characters

ValueCountFrequency (%)
r 819
13.2%
e 737
11.9%
i 715
11.5%
a 409
 
6.6%
s 408
 
6.6%
o 396
 
6.4%
n 390
 
6.3%
h 322
 
5.2%
314
 
5.1%
t 246
 
4.0%
Other values (13) 1450
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 819
13.2%
e 737
11.9%
i 715
11.5%
a 409
 
6.6%
s 408
 
6.6%
o 396
 
6.4%
n 390
 
6.3%
h 322
 
5.2%
314
 
5.1%
t 246
 
4.0%
Other values (13) 1450
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 819
13.2%
e 737
11.9%
i 715
11.5%
a 409
 
6.6%
s 408
 
6.6%
o 396
 
6.4%
n 390
 
6.3%
h 322
 
5.2%
314
 
5.1%
t 246
 
4.0%
Other values (13) 1450
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 819
13.2%
e 737
11.9%
i 715
11.5%
a 409
 
6.6%
s 408
 
6.6%
o 396
 
6.4%
n 390
 
6.3%
h 322
 
5.2%
314
 
5.1%
t 246
 
4.0%
Other values (13) 1450
23.4%

sub_type
Categorical

High correlation  Missing 

Distinct15
Distinct (%)3.1%
Missing15
Missing (%)3.0%
Memory size32.7 KiB
Bottom Loading
46 
Top Load
44 
Full Size
42 
Table Top
39 
Top Loading
38 
Other values (10)
276 

Length

Max length14
Median length11
Mean length9.7051546
Min length5

Characters and Unicode

Total characters4707
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTable Top
2nd rowTop Load
3rd rowInverter AC
4th rowWindow AC
5th rowSplit AC

Common Values

ValueCountFrequency (%)
Bottom Loading 46
 
9.2%
Top Load 44
 
8.8%
Full Size 42
 
8.4%
Table Top 39
 
7.8%
Top Loading 38
 
7.6%
Front Load 33
 
6.6%
Personal 31
 
6.2%
Desert 31
 
6.2%
Side-by-Side 30
 
6.0%
Double Door 30
 
6.0%
Other values (5) 121
24.2%

Length

2025-08-19T01:03:03.313010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
top 121
14.1%
loading 84
 
9.8%
load 77
 
8.9%
ac 76
 
8.8%
door 58
 
6.7%
bottom 46
 
5.3%
full 42
 
4.9%
size 42
 
4.9%
table 39
 
4.5%
front 33
 
3.8%
Other values (9) 243
28.2%

Most occurring characters

ValueCountFrequency (%)
o 626
 
13.3%
376
 
8.0%
e 365
 
7.8%
i 262
 
5.6%
d 246
 
5.2%
l 235
 
5.0%
a 231
 
4.9%
n 229
 
4.9%
r 226
 
4.8%
t 207
 
4.4%
Other values (22) 1704
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4707
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 626
 
13.3%
376
 
8.0%
e 365
 
7.8%
i 262
 
5.6%
d 246
 
5.2%
l 235
 
5.0%
a 231
 
4.9%
n 229
 
4.9%
r 226
 
4.8%
t 207
 
4.4%
Other values (22) 1704
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4707
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 626
 
13.3%
376
 
8.0%
e 365
 
7.8%
i 262
 
5.6%
d 246
 
5.2%
l 235
 
5.0%
a 231
 
4.9%
n 229
 
4.9%
r 226
 
4.8%
t 207
 
4.4%
Other values (22) 1704
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4707
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 626
 
13.3%
376
 
8.0%
e 365
 
7.8%
i 262
 
5.6%
d 246
 
5.2%
l 235
 
5.0%
a 231
 
4.9%
n 229
 
4.9%
r 226
 
4.8%
t 207
 
4.4%
Other values (22) 1704
36.2%

model_name
Text

Missing 

Distinct485
Distinct (%)100.0%
Missing15
Missing (%)3.0%
Memory size32.2 KiB
2025-08-19T01:03:03.581317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7793814
Min length8

Characters and Unicode

Total characters4743
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique485 ?
Unique (%)100.0%

Sample

1st rowModel_N299
2nd rowModel_K244
3rd rowModel_X75
4th rowModel_X101
5th rowModel_Y128
ValueCountFrequency (%)
model_z233 1
 
0.2%
model_e498 1
 
0.2%
model_s460 1
 
0.2%
model_k218 1
 
0.2%
model_b469 1
 
0.2%
model_n65 1
 
0.2%
model_j217 1
 
0.2%
model_r459 1
 
0.2%
model_o274 1
 
0.2%
model_s486 1
 
0.2%
Other values (475) 475
97.9%
2025-08-19T01:03:03.988452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 504
10.6%
o 485
10.2%
d 485
10.2%
e 485
10.2%
l 485
10.2%
_ 485
10.2%
1 198
 
4.2%
2 195
 
4.1%
3 191
 
4.0%
4 190
 
4.0%
Other values (31) 1040
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 504
10.6%
o 485
10.2%
d 485
10.2%
e 485
10.2%
l 485
10.2%
_ 485
10.2%
1 198
 
4.2%
2 195
 
4.1%
3 191
 
4.0%
4 190
 
4.0%
Other values (31) 1040
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 504
10.6%
o 485
10.2%
d 485
10.2%
e 485
10.2%
l 485
10.2%
_ 485
10.2%
1 198
 
4.2%
2 195
 
4.1%
3 191
 
4.0%
4 190
 
4.0%
Other values (31) 1040
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 504
10.6%
o 485
10.2%
d 485
10.2%
e 485
10.2%
l 485
10.2%
_ 485
10.2%
1 198
 
4.2%
2 195
 
4.1%
3 191
 
4.0%
4 190
 
4.0%
Other values (31) 1040
21.9%

capacity_tons
Categorical

High correlation  Missing 

Distinct4
Distinct (%)5.2%
Missing423
Missing (%)84.6%
Memory size31.1 KiB
1.5
26 
2.0
18 
1.2
17 
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters231
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.5

Common Values

ValueCountFrequency (%)
1.5 26
 
5.2%
2.0 18
 
3.6%
1.2 17
 
3.4%
1.0 16
 
3.2%
(Missing) 423
84.6%

Length

2025-08-19T01:03:04.104186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:04.176021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.5 26
33.8%
2.0 18
23.4%
1.2 17
22.1%
1.0 16
20.8%

Most occurring characters

ValueCountFrequency (%)
. 77
33.3%
1 59
25.5%
2 35
15.2%
0 34
14.7%
5 26
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 77
33.3%
1 59
25.5%
2 35
15.2%
0 34
14.7%
5 26
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 77
33.3%
1 59
25.5%
2 35
15.2%
0 34
14.7%
5 26
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 77
33.3%
1 59
25.5%
2 35
15.2%
0 34
14.7%
5 26
 
11.3%

capacity_liters
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)4.0%
Missing247
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean153.16206
Minimum15
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:03:04.259815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q120
median50
Q3250
95-th percentile550
Maximum550
Range535
Interquartile range (IQR)230

Descriptive statistics

Standard deviation180.98847
Coefficient of variation (CV)1.1816796
Kurtosis-0.14333813
Mean153.16206
Median Absolute Deviation (MAD)35
Skewness1.1452337
Sum38750
Variance32756.827
MonotonicityNot monotonic
2025-08-19T01:03:04.551268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20 61
 
12.2%
15 46
 
9.2%
50 25
 
5.0%
250 24
 
4.8%
550 23
 
4.6%
450 17
 
3.4%
90 17
 
3.4%
70 15
 
3.0%
350 13
 
2.6%
180 12
 
2.4%
(Missing) 247
49.4%
ValueCountFrequency (%)
15 46
9.2%
20 61
12.2%
50 25
5.0%
70 15
 
3.0%
90 17
 
3.4%
180 12
 
2.4%
250 24
 
4.8%
350 13
 
2.6%
450 17
 
3.4%
550 23
 
4.6%
ValueCountFrequency (%)
550 23
 
4.6%
450 17
 
3.4%
350 13
 
2.6%
250 24
 
4.8%
180 12
 
2.4%
90 17
 
3.4%
70 15
 
3.0%
50 25
5.0%
20 61
12.2%
15 46
9.2%

capacity_kg
Categorical

High correlation  Missing 

Distinct5
Distinct (%)6.5%
Missing423
Missing (%)84.6%
Memory size31.1 KiB
7.0
20 
6.5
19 
8.0
16 
9.0
12 
6.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters231
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.5
2nd row6.5
3rd row9.0
4th row6.5
5th row8.0

Common Values

ValueCountFrequency (%)
7.0 20
 
4.0%
6.5 19
 
3.8%
8.0 16
 
3.2%
9.0 12
 
2.4%
6.0 10
 
2.0%
(Missing) 423
84.6%

Length

2025-08-19T01:03:04.652248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:04.734769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.0 20
26.0%
6.5 19
24.7%
8.0 16
20.8%
9.0 12
15.6%
6.0 10
13.0%

Most occurring characters

ValueCountFrequency (%)
. 77
33.3%
0 58
25.1%
6 29
 
12.6%
7 20
 
8.7%
5 19
 
8.2%
8 16
 
6.9%
9 12
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 77
33.3%
0 58
25.1%
6 29
 
12.6%
7 20
 
8.7%
5 19
 
8.2%
8 16
 
6.9%
9 12
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 77
33.3%
0 58
25.1%
6 29
 
12.6%
7 20
 
8.7%
5 19
 
8.2%
8 16
 
6.9%
9 12
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 77
33.3%
0 58
25.1%
6 29
 
12.6%
7 20
 
8.7%
5 19
 
8.2%
8 16
 
6.9%
9 12
 
5.2%

capacity_place_settings
Categorical

High correlation  Missing 

Distinct4
Distinct (%)4.9%
Missing418
Missing (%)83.6%
Memory size31.1 KiB
12.0
25 
14.0
22 
8.0
19 
15.0
16 

Length

Max length4
Median length4
Mean length3.7682927
Min length3

Characters and Unicode

Total characters309
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.0
2nd row8.0
3rd row14.0
4th row15.0
5th row12.0

Common Values

ValueCountFrequency (%)
12.0 25
 
5.0%
14.0 22
 
4.4%
8.0 19
 
3.8%
15.0 16
 
3.2%
(Missing) 418
83.6%

Length

2025-08-19T01:03:04.843853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:04.939284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12.0 25
30.5%
14.0 22
26.8%
8.0 19
23.2%
15.0 16
19.5%

Most occurring characters

ValueCountFrequency (%)
. 82
26.5%
0 82
26.5%
1 63
20.4%
2 25
 
8.1%
4 22
 
7.1%
8 19
 
6.1%
5 16
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 82
26.5%
0 82
26.5%
1 63
20.4%
2 25
 
8.1%
4 22
 
7.1%
8 19
 
6.1%
5 16
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 82
26.5%
0 82
26.5%
1 63
20.4%
2 25
 
8.1%
4 22
 
7.1%
8 19
 
6.1%
5 16
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 82
26.5%
0 82
26.5%
1 63
20.4%
2 25
 
8.1%
4 22
 
7.1%
8 19
 
6.1%
5 16
 
5.2%

technology
Categorical

High correlation  Missing 

Distinct9
Distinct (%)1.9%
Missing15
Missing (%)3.0%
Memory size35.2 KiB
Compressor Cooling
86 
Electronic Control
82 
Evaporative Cooling
77 
Direct Cool
50 
Non-Inverter
47 
Other values (4)
143 

Length

Max length19
Median length18
Mean length15.080412
Min length8

Characters and Unicode

Total characters7314
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectronic Control
2nd rowFully-Automatic
3rd rowInverter
4th rowNon-Inverter
5th rowNon-Inverter

Common Values

ValueCountFrequency (%)
Compressor Cooling 86
17.2%
Electronic Control 82
16.4%
Evaporative Cooling 77
15.4%
Direct Cool 50
10.0%
Non-Inverter 47
9.4%
Frost Free 40
8.0%
Fully-Automatic 39
7.8%
Semi-Automatic 36
7.2%
Inverter 28
 
5.6%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:05.040008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:05.142432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cooling 163
19.9%
compressor 86
10.5%
electronic 82
10.0%
control 82
10.0%
evaporative 77
9.4%
direct 50
 
6.1%
cool 50
 
6.1%
non-inverter 47
 
5.7%
frost 40
 
4.9%
free 40
 
4.9%
Other values (3) 103
12.6%

Most occurring characters

ValueCountFrequency (%)
o 1083
14.8%
r 693
 
9.5%
e 561
 
7.7%
t 556
 
7.6%
i 483
 
6.6%
l 455
 
6.2%
n 449
 
6.1%
C 381
 
5.2%
335
 
4.6%
c 289
 
4.0%
Other values (16) 2029
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1083
14.8%
r 693
 
9.5%
e 561
 
7.7%
t 556
 
7.6%
i 483
 
6.6%
l 455
 
6.2%
n 449
 
6.1%
C 381
 
5.2%
335
 
4.6%
c 289
 
4.0%
Other values (16) 2029
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1083
14.8%
r 693
 
9.5%
e 561
 
7.7%
t 556
 
7.6%
i 483
 
6.6%
l 455
 
6.2%
n 449
 
6.1%
C 381
 
5.2%
335
 
4.6%
c 289
 
4.0%
Other values (16) 2029
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1083
14.8%
r 693
 
9.5%
e 561
 
7.7%
t 556
 
7.6%
i 483
 
6.6%
l 455
 
6.2%
n 449
 
6.1%
C 381
 
5.2%
335
 
4.6%
c 289
 
4.0%
Other values (16) 2029
27.7%

feature_1
Categorical

High correlation  Missing 

Distinct17
Distinct (%)3.5%
Missing15
Missing (%)3.0%
Memory size35.2 KiB
ProSmart Inverter Motor
62 
Hot & Cold
33 
StoreFresh+
33 
Hygiene+
30 
Hot, Normal & Cold
30 
Other values (12)
297 

Length

Max length23
Median length18
Mean length15
Min length8

Characters and Unicode

Total characters7275
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProSmart Inverter Motor
2nd rowProSmart Inverter Motor
3rd rowTurbo Mode
4th rowSelf Diagnosis
5th rowTurbo Mode

Common Values

ValueCountFrequency (%)
ProSmart Inverter Motor 62
 
12.4%
Hot & Cold 33
 
6.6%
StoreFresh+ 33
 
6.6%
Hygiene+ 30
 
6.0%
Hot, Normal & Cold 30
 
6.0%
Turbo Mode 29
 
5.8%
NeoFrost Dual Cooling 29
 
5.8%
GentleWave Drum 28
 
5.6%
Remote Control 28
 
5.6%
Active Fresh Blue Light 27
 
5.4%
Other values (7) 156
31.2%

Length

2025-08-19T01:03:05.298802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
87
 
7.7%
cold 87
 
7.7%
hot 63
 
5.6%
motor 62
 
5.5%
inverter 62
 
5.5%
prosmart 62
 
5.5%
normal 54
 
4.8%
cooling 52
 
4.6%
storefresh 33
 
2.9%
hygiene 30
 
2.6%
Other values (21) 541
47.7%

Most occurring characters

ValueCountFrequency (%)
o 801
 
11.0%
e 707
 
9.7%
648
 
8.9%
r 595
 
8.2%
t 508
 
7.0%
l 352
 
4.8%
a 319
 
4.4%
n 293
 
4.0%
m 235
 
3.2%
s 221
 
3.0%
Other values (31) 2596
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 801
 
11.0%
e 707
 
9.7%
648
 
8.9%
r 595
 
8.2%
t 508
 
7.0%
l 352
 
4.8%
a 319
 
4.4%
n 293
 
4.0%
m 235
 
3.2%
s 221
 
3.0%
Other values (31) 2596
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 801
 
11.0%
e 707
 
9.7%
648
 
8.9%
r 595
 
8.2%
t 508
 
7.0%
l 352
 
4.8%
a 319
 
4.4%
n 293
 
4.0%
m 235
 
3.2%
s 221
 
3.0%
Other values (31) 2596
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 801
 
11.0%
e 707
 
9.7%
648
 
8.9%
r 595
 
8.2%
t 508
 
7.0%
l 352
 
4.8%
a 319
 
4.4%
n 293
 
4.0%
m 235
 
3.2%
s 221
 
3.0%
Other values (31) 2596
35.7%

energy_rating_stars
Categorical

Missing 

Distinct5
Distinct (%)1.2%
Missing90
Missing (%)18.0%
Memory size29.8 KiB
3.0
88 
5.0
87 
2.0
81 
4.0
79 
1.0
75 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1230
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 88
17.6%
5.0 87
17.4%
2.0 81
16.2%
4.0 79
15.8%
1.0 75
15.0%
(Missing) 90
18.0%

Length

2025-08-19T01:03:05.398537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:05.471807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 88
21.5%
5.0 87
21.2%
2.0 81
19.8%
4.0 79
19.3%
1.0 75
18.3%

Most occurring characters

ValueCountFrequency (%)
. 410
33.3%
0 410
33.3%
3 88
 
7.2%
5 87
 
7.1%
2 81
 
6.6%
4 79
 
6.4%
1 75
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 410
33.3%
0 410
33.3%
3 88
 
7.2%
5 87
 
7.1%
2 81
 
6.6%
4 79
 
6.4%
1 75
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 410
33.3%
0 410
33.3%
3 88
 
7.2%
5 87
 
7.1%
2 81
 
6.6%
4 79
 
6.4%
1 75
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 410
33.3%
0 410
33.3%
3 88
 
7.2%
5 87
 
7.1%
2 81
 
6.6%
4 79
 
6.4%
1 75
 
6.1%

color
Categorical

Missing 

Distinct5
Distinct (%)1.0%
Missing15
Missing (%)3.0%
Memory size30.3 KiB
Grey
105 
Silver
102 
White
97 
Blue
95 
Black
86 

Length

Max length6
Median length5
Mean length4.7979381
Min length4

Characters and Unicode

Total characters2327
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowSilver
3rd rowBlue
4th rowGrey
5th rowBlack

Common Values

ValueCountFrequency (%)
Grey 105
21.0%
Silver 102
20.4%
White 97
19.4%
Blue 95
19.0%
Black 86
17.2%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:05.584913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:05.670823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
grey 105
21.6%
silver 102
21.0%
white 97
20.0%
blue 95
19.6%
black 86
17.7%

Most occurring characters

ValueCountFrequency (%)
e 399
17.1%
l 283
12.2%
r 207
 
8.9%
i 199
 
8.6%
B 181
 
7.8%
y 105
 
4.5%
G 105
 
4.5%
S 102
 
4.4%
v 102
 
4.4%
W 97
 
4.2%
Other values (6) 547
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 399
17.1%
l 283
12.2%
r 207
 
8.9%
i 199
 
8.6%
B 181
 
7.8%
y 105
 
4.5%
G 105
 
4.5%
S 102
 
4.4%
v 102
 
4.4%
W 97
 
4.2%
Other values (6) 547
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 399
17.1%
l 283
12.2%
r 207
 
8.9%
i 199
 
8.6%
B 181
 
7.8%
y 105
 
4.5%
G 105
 
4.5%
S 102
 
4.4%
v 102
 
4.4%
W 97
 
4.2%
Other values (6) 547
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 399
17.1%
l 283
12.2%
r 207
 
8.9%
i 199
 
8.6%
B 181
 
7.8%
y 105
 
4.5%
G 105
 
4.5%
S 102
 
4.4%
v 102
 
4.4%
W 97
 
4.2%
Other values (6) 547
23.5%

price_inr
Real number (ℝ)

Missing 

Distinct484
Distinct (%)99.8%
Missing15
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean46567.21
Minimum8351
Maximum84989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:03:05.789363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8351
5-th percentile10859.8
Q125661
median46386
Q367550
95-th percentile81846.8
Maximum84989
Range76638
Interquartile range (IQR)41889

Descriptive statistics

Standard deviation23234.383
Coefficient of variation (CV)0.49894299
Kurtosis-1.3193355
Mean46567.21
Median Absolute Deviation (MAD)21151
Skewness-0.0056022845
Sum22585097
Variance5.3983655 × 108
MonotonicityNot monotonic
2025-08-19T01:03:05.932976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16595 2
 
0.4%
84989 1
 
0.2%
70115 1
 
0.2%
61826 1
 
0.2%
18381 1
 
0.2%
65780 1
 
0.2%
58709 1
 
0.2%
20069 1
 
0.2%
34240 1
 
0.2%
60659 1
 
0.2%
Other values (474) 474
94.8%
(Missing) 15
 
3.0%
ValueCountFrequency (%)
8351 1
0.2%
8437 1
0.2%
8906 1
0.2%
9064 1
0.2%
9148 1
0.2%
9247 1
0.2%
9258 1
0.2%
9309 1
0.2%
9373 1
0.2%
9694 1
0.2%
ValueCountFrequency (%)
84989 1
0.2%
84931 1
0.2%
84786 1
0.2%
84533 1
0.2%
84352 1
0.2%
84281 1
0.2%
84196 1
0.2%
83970 1
0.2%
83862 1
0.2%
83766 1
0.2%

manufacturing_date
Date

Missing 

Distinct377
Distinct (%)77.7%
Missing15
Missing (%)3.0%
Memory size4.0 KiB
Minimum2022-01-02 00:00:00
Maximum2024-12-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-19T01:03:06.081896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:03:06.230226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

warranty_years
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.8%
Missing15
Missing (%)3.0%
Memory size29.6 KiB
2.0
137 
10.0
122 
1.0
114 
5.0
112 

Length

Max length4
Median length3
Mean length3.2515464
Min length3

Characters and Unicode

Total characters1577
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row1.0
4th row2.0
5th row10.0

Common Values

ValueCountFrequency (%)
2.0 137
27.4%
10.0 122
24.4%
1.0 114
22.8%
5.0 112
22.4%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:06.355183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:06.439567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 137
28.2%
10.0 122
25.2%
1.0 114
23.5%
5.0 112
23.1%

Most occurring characters

ValueCountFrequency (%)
0 607
38.5%
. 485
30.8%
1 236
 
15.0%
2 137
 
8.7%
5 112
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 607
38.5%
. 485
30.8%
1 236
 
15.0%
2 137
 
8.7%
5 112
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 607
38.5%
. 485
30.8%
1 236
 
15.0%
2 137
 
8.7%
5 112
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 607
38.5%
. 485
30.8%
1 236
 
15.0%
2 137
 
8.7%
5 112
 
7.1%

customer_rating
Real number (ℝ)

Missing 

Distinct21
Distinct (%)4.3%
Missing15
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean3.9971134
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:03:06.537504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.1
Q13.5
median4
Q34.5
95-th percentile4.9
Maximum5
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.59072717
Coefficient of variation (CV)0.14778844
Kurtosis-1.1993161
Mean3.9971134
Median Absolute Deviation (MAD)0.5
Skewness0.010956278
Sum1938.6
Variance0.34895859
MonotonicityNot monotonic
2025-08-19T01:03:06.649978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.6 33
 
6.6%
3.1 32
 
6.4%
3.8 29
 
5.8%
4 29
 
5.8%
4.9 26
 
5.2%
4.8 26
 
5.2%
3.4 26
 
5.2%
4.4 25
 
5.0%
4.2 24
 
4.8%
3.3 24
 
4.8%
Other values (11) 211
42.2%
ValueCountFrequency (%)
3 11
 
2.2%
3.1 32
6.4%
3.2 24
4.8%
3.3 24
4.8%
3.4 26
5.2%
3.5 21
4.2%
3.6 17
3.4%
3.7 23
4.6%
3.8 29
5.8%
3.9 23
4.6%
ValueCountFrequency (%)
5 15
3.0%
4.9 26
5.2%
4.8 26
5.2%
4.7 17
3.4%
4.6 33
6.6%
4.5 17
3.4%
4.4 25
5.0%
4.3 19
3.8%
4.2 24
4.8%
4.1 24
4.8%

city
Categorical

Missing 

Distinct7
Distinct (%)1.4%
Missing15
Missing (%)3.0%
Memory size31.2 KiB
Pune
83 
Hyderabad
71 
Mumbai
70 
Kolkata
69 
Chennai
68 
Other values (2)
124 

Length

Max length9
Median length7
Mean length6.5938144
Min length4

Characters and Unicode

Total characters3198
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowDelhi
3rd rowBangalore
4th rowHyderabad
5th rowBangalore

Common Values

ValueCountFrequency (%)
Pune 83
16.6%
Hyderabad 71
14.2%
Mumbai 70
14.0%
Kolkata 69
13.8%
Chennai 68
13.6%
Delhi 67
13.4%
Bangalore 57
11.4%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:06.769778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:06.885429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pune 83
17.1%
hyderabad 71
14.6%
mumbai 70
14.4%
kolkata 69
14.2%
chennai 68
14.0%
delhi 67
13.8%
bangalore 57
11.8%

Most occurring characters

ValueCountFrequency (%)
a 532
16.6%
e 346
 
10.8%
n 276
 
8.6%
i 205
 
6.4%
l 193
 
6.0%
u 153
 
4.8%
d 142
 
4.4%
b 141
 
4.4%
h 135
 
4.2%
r 128
 
4.0%
Other values (13) 947
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 532
16.6%
e 346
 
10.8%
n 276
 
8.6%
i 205
 
6.4%
l 193
 
6.0%
u 153
 
4.8%
d 142
 
4.4%
b 141
 
4.4%
h 135
 
4.2%
r 128
 
4.0%
Other values (13) 947
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 532
16.6%
e 346
 
10.8%
n 276
 
8.6%
i 205
 
6.4%
l 193
 
6.0%
u 153
 
4.8%
d 142
 
4.4%
b 141
 
4.4%
h 135
 
4.2%
r 128
 
4.0%
Other values (13) 947
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 532
16.6%
e 346
 
10.8%
n 276
 
8.6%
i 205
 
6.4%
l 193
 
6.0%
u 153
 
4.8%
d 142
 
4.4%
b 141
 
4.4%
h 135
 
4.2%
r 128
 
4.0%
Other values (13) 947
29.6%

platform
Categorical

Missing 

Distinct4
Distinct (%)0.8%
Missing15
Missing (%)3.0%
Memory size31.6 KiB
Amazon
124 
Croma
122 
Vijay Sales
120 
Flipkart
119 

Length

Max length11
Median length8
Mean length7.4762887
Min length5

Characters and Unicode

Total characters3626
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmazon
2nd rowAmazon
3rd rowFlipkart
4th rowVijay Sales
5th rowAmazon

Common Values

ValueCountFrequency (%)
Amazon 124
24.8%
Croma 122
24.4%
Vijay Sales 120
24.0%
Flipkart 119
23.8%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:07.032643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:07.118168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
amazon 124
20.5%
croma 122
20.2%
vijay 120
19.8%
sales 120
19.8%
flipkart 119
19.7%

Most occurring characters

ValueCountFrequency (%)
a 605
16.7%
o 246
 
6.8%
m 246
 
6.8%
r 241
 
6.6%
i 239
 
6.6%
l 239
 
6.6%
A 124
 
3.4%
z 124
 
3.4%
n 124
 
3.4%
C 122
 
3.4%
Other values (11) 1316
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 605
16.7%
o 246
 
6.8%
m 246
 
6.8%
r 241
 
6.6%
i 239
 
6.6%
l 239
 
6.6%
A 124
 
3.4%
z 124
 
3.4%
n 124
 
3.4%
C 122
 
3.4%
Other values (11) 1316
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 605
16.7%
o 246
 
6.8%
m 246
 
6.8%
r 241
 
6.6%
i 239
 
6.6%
l 239
 
6.6%
A 124
 
3.4%
z 124
 
3.4%
n 124
 
3.4%
C 122
 
3.4%
Other values (11) 1316
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 605
16.7%
o 246
 
6.8%
m 246
 
6.8%
r 241
 
6.6%
i 239
 
6.6%
l 239
 
6.6%
A 124
 
3.4%
z 124
 
3.4%
n 124
 
3.4%
C 122
 
3.4%
Other values (11) 1316
36.3%

discount_offered
Real number (ℝ)

Missing  Zeros 

Distinct7
Distinct (%)1.4%
Missing15
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean15.164948
Minimum0
Maximum30
Zeros63
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:03:07.202014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q325
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.957223
Coefficient of variation (CV)0.65659458
Kurtosis-1.2484198
Mean15.164948
Median Absolute Deviation (MAD)10
Skewness-0.0047246989
Sum7355
Variance99.14629
MonotonicityNot monotonic
2025-08-19T01:03:07.299009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 76
15.2%
15 75
15.0%
30 71
14.2%
25 70
14.0%
20 67
13.4%
10 63
12.6%
0 63
12.6%
(Missing) 15
 
3.0%
ValueCountFrequency (%)
0 63
12.6%
5 76
15.2%
10 63
12.6%
15 75
15.0%
20 67
13.4%
25 70
14.0%
30 71
14.2%
ValueCountFrequency (%)
30 71
14.2%
25 70
14.0%
20 67
13.4%
15 75
15.0%
10 63
12.6%
5 76
15.2%
0 63
12.6%

availability
Categorical

Missing 

Distinct2
Distinct (%)0.4%
Missing15
Missing (%)3.0%
Memory size32.8 KiB
Out of Stock
250 
In Stock
235 

Length

Max length12
Median length12
Mean length10.061856
Min length8

Characters and Unicode

Total characters4880
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOut of Stock
2nd rowOut of Stock
3rd rowIn Stock
4th rowOut of Stock
5th rowIn Stock

Common Values

ValueCountFrequency (%)
Out of Stock 250
50.0%
In Stock 235
47.0%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:07.409799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:07.480241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
stock 485
39.8%
out 250
20.5%
of 250
20.5%
in 235
19.3%

Most occurring characters

ValueCountFrequency (%)
t 735
15.1%
o 735
15.1%
735
15.1%
k 485
9.9%
c 485
9.9%
S 485
9.9%
O 250
 
5.1%
u 250
 
5.1%
f 250
 
5.1%
I 235
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 735
15.1%
o 735
15.1%
735
15.1%
k 485
9.9%
c 485
9.9%
S 485
9.9%
O 250
 
5.1%
u 250
 
5.1%
f 250
 
5.1%
I 235
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 735
15.1%
o 735
15.1%
735
15.1%
k 485
9.9%
c 485
9.9%
S 485
9.9%
O 250
 
5.1%
u 250
 
5.1%
f 250
 
5.1%
I 235
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 735
15.1%
o 735
15.1%
735
15.1%
k 485
9.9%
c 485
9.9%
S 485
9.9%
O 250
 
5.1%
u 250
 
5.1%
f 250
 
5.1%
I 235
 
4.8%

warranty_duration_months
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.8%
Missing15
Missing (%)3.0%
Memory size30.1 KiB
24.0
134 
120.0
125 
12.0
113 
60.0
113 

Length

Max length5
Median length4
Mean length4.257732
Min length4

Characters and Unicode

Total characters2065
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120.0
2nd row120.0
3rd row12.0
4th row24.0
5th row120.0

Common Values

ValueCountFrequency (%)
24.0 134
26.8%
120.0 125
25.0%
12.0 113
22.6%
60.0 113
22.6%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:07.565679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:07.643446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
24.0 134
27.6%
120.0 125
25.8%
12.0 113
23.3%
60.0 113
23.3%

Most occurring characters

ValueCountFrequency (%)
0 723
35.0%
. 485
23.5%
2 372
18.0%
1 238
 
11.5%
4 134
 
6.5%
6 113
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 723
35.0%
. 485
23.5%
2 372
18.0%
1 238
 
11.5%
4 134
 
6.5%
6 113
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 723
35.0%
. 485
23.5%
2 372
18.0%
1 238
 
11.5%
4 134
 
6.5%
6 113
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 723
35.0%
. 485
23.5%
2 372
18.0%
1 238
 
11.5%
4 134
 
6.5%
6 113
 
5.5%

review_sentiment
Categorical

Missing 

Distinct3
Distinct (%)0.6%
Missing15
Missing (%)3.0%
Memory size31.7 KiB
Neutral
168 
Negative
163 
Positive
154 

Length

Max length8
Median length8
Mean length7.6536082
Min length7

Characters and Unicode

Total characters3712
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowNeutral
5th rowPositive

Common Values

ValueCountFrequency (%)
Neutral 168
33.6%
Negative 163
32.6%
Positive 154
30.8%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:07.743407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:07.816245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
neutral 168
34.6%
negative 163
33.6%
positive 154
31.8%

Most occurring characters

ValueCountFrequency (%)
e 648
17.5%
t 485
13.1%
i 471
12.7%
a 331
8.9%
N 331
8.9%
v 317
8.5%
u 168
 
4.5%
r 168
 
4.5%
l 168
 
4.5%
g 163
 
4.4%
Other values (3) 462
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 648
17.5%
t 485
13.1%
i 471
12.7%
a 331
8.9%
N 331
8.9%
v 317
8.5%
u 168
 
4.5%
r 168
 
4.5%
l 168
 
4.5%
g 163
 
4.4%
Other values (3) 462
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 648
17.5%
t 485
13.1%
i 471
12.7%
a 331
8.9%
N 331
8.9%
v 317
8.5%
u 168
 
4.5%
r 168
 
4.5%
l 168
 
4.5%
g 163
 
4.4%
Other values (3) 462
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 648
17.5%
t 485
13.1%
i 471
12.7%
a 331
8.9%
N 331
8.9%
v 317
8.5%
u 168
 
4.5%
r 168
 
4.5%
l 168
 
4.5%
g 163
 
4.4%
Other values (3) 462
12.4%

return_status
Categorical

Missing 

Distinct2
Distinct (%)0.4%
Missing15
Missing (%)3.0%
Memory size32.8 KiB
Not Returned
253 
Returned
232 

Length

Max length12
Median length12
Mean length10.086598
Min length8

Characters and Unicode

Total characters4892
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Returned
2nd rowReturned
3rd rowNot Returned
4th rowNot Returned
5th rowReturned

Common Values

ValueCountFrequency (%)
Not Returned 253
50.6%
Returned 232
46.4%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:07.929972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:08.004124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
returned 485
65.7%
not 253
34.3%

Most occurring characters

ValueCountFrequency (%)
e 970
19.8%
t 738
15.1%
n 485
9.9%
r 485
9.9%
R 485
9.9%
u 485
9.9%
d 485
9.9%
N 253
 
5.2%
o 253
 
5.2%
253
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 970
19.8%
t 738
15.1%
n 485
9.9%
r 485
9.9%
R 485
9.9%
u 485
9.9%
d 485
9.9%
N 253
 
5.2%
o 253
 
5.2%
253
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 970
19.8%
t 738
15.1%
n 485
9.9%
r 485
9.9%
R 485
9.9%
u 485
9.9%
d 485
9.9%
N 253
 
5.2%
o 253
 
5.2%
253
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 970
19.8%
t 738
15.1%
n 485
9.9%
r 485
9.9%
R 485
9.9%
u 485
9.9%
d 485
9.9%
N 253
 
5.2%
o 253
 
5.2%
253
 
5.2%

complaint_text
Categorical

Missing 

Distinct7
Distinct (%)1.4%
Missing15
Missing (%)3.0%
Memory size39.8 KiB
Excellent performance.
87 
Slow installation service.
73 
Delivered with damaged packaging.
72 
No issues so far.
68 
Remote stopped working.
66 
Other values (2)
119 

Length

Max length33
Median length25
Mean length24.884536
Min length17

Characters and Unicode

Total characters12069
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelivered with damaged packaging.
2nd rowCooling issue after 2 months.
3rd rowDelivered with damaged packaging.
4th rowNoisy operation at night.
5th rowExcellent performance.

Common Values

ValueCountFrequency (%)
Excellent performance. 87
17.4%
Slow installation service. 73
14.6%
Delivered with damaged packaging. 72
14.4%
No issues so far. 68
13.6%
Remote stopped working. 66
13.2%
Noisy operation at night. 61
12.2%
Cooling issue after 2 months. 58
11.6%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:08.384626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:08.482739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
excellent 87
 
5.2%
performance 87
 
5.2%
slow 73
 
4.3%
installation 73
 
4.3%
service 73
 
4.3%
delivered 72
 
4.3%
with 72
 
4.3%
damaged 72
 
4.3%
packaging 72
 
4.3%
no 68
 
4.0%
Other values (15) 936
55.5%

Most occurring characters

ValueCountFrequency (%)
e 1225
 
10.1%
1200
 
9.9%
o 924
 
7.7%
i 868
 
7.2%
a 769
 
6.4%
t 736
 
6.1%
s 719
 
6.0%
n 696
 
5.8%
r 572
 
4.7%
l 523
 
4.3%
Other values (21) 3837
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12069
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1225
 
10.1%
1200
 
9.9%
o 924
 
7.7%
i 868
 
7.2%
a 769
 
6.4%
t 736
 
6.1%
s 719
 
6.0%
n 696
 
5.8%
r 572
 
4.7%
l 523
 
4.3%
Other values (21) 3837
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12069
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1225
 
10.1%
1200
 
9.9%
o 924
 
7.7%
i 868
 
7.2%
a 769
 
6.4%
t 736
 
6.1%
s 719
 
6.0%
n 696
 
5.8%
r 572
 
4.7%
l 523
 
4.3%
Other values (21) 3837
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12069
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1225
 
10.1%
1200
 
9.9%
o 924
 
7.7%
i 868
 
7.2%
a 769
 
6.4%
t 736
 
6.1%
s 719
 
6.0%
n 696
 
5.8%
r 572
 
4.7%
l 523
 
4.3%
Other values (21) 3837
31.8%

resolved_status
Categorical

Missing 

Distinct3
Distinct (%)0.6%
Missing15
Missing (%)3.0%
Memory size32.7 KiB
In Progress
169 
Unresolved
160 
Resolved
156 

Length

Max length11
Median length10
Mean length9.7051546
Min length8

Characters and Unicode

Total characters4707
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResolved
2nd rowIn Progress
3rd rowUnresolved
4th rowResolved
5th rowResolved

Common Values

ValueCountFrequency (%)
In Progress 169
33.8%
Unresolved 160
32.0%
Resolved 156
31.2%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:08.624179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:08.698582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
in 169
25.8%
progress 169
25.8%
unresolved 160
24.5%
resolved 156
23.9%

Most occurring characters

ValueCountFrequency (%)
e 801
17.0%
s 654
13.9%
r 498
10.6%
o 485
10.3%
n 329
7.0%
v 316
 
6.7%
d 316
 
6.7%
l 316
 
6.7%
I 169
 
3.6%
P 169
 
3.6%
Other values (4) 654
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4707
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 801
17.0%
s 654
13.9%
r 498
10.6%
o 485
10.3%
n 329
7.0%
v 316
 
6.7%
d 316
 
6.7%
l 316
 
6.7%
I 169
 
3.6%
P 169
 
3.6%
Other values (4) 654
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4707
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 801
17.0%
s 654
13.9%
r 498
10.6%
o 485
10.3%
n 329
7.0%
v 316
 
6.7%
d 316
 
6.7%
l 316
 
6.7%
I 169
 
3.6%
P 169
 
3.6%
Other values (4) 654
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4707
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 801
17.0%
s 654
13.9%
r 498
10.6%
o 485
10.3%
n 329
7.0%
v 316
 
6.7%
d 316
 
6.7%
l 316
 
6.7%
I 169
 
3.6%
P 169
 
3.6%
Other values (4) 654
13.9%

review_date
Date

Missing 

Distinct371
Distinct (%)76.5%
Missing15
Missing (%)3.0%
Memory size4.0 KiB
Minimum2022-01-01 00:00:00
Maximum2024-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-19T01:03:08.804569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:03:08.951800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviewer_location
Categorical

Missing 

Distinct7
Distinct (%)1.4%
Missing15
Missing (%)3.0%
Memory size31.4 KiB
Hyderabad
87 
Ahmedabad
79 
Chennai
74 
Mumbai
65 
Pune
63 
Other values (2)
117 

Length

Max length9
Median length7
Mean length7.1319588
Min length4

Characters and Unicode

Total characters3459
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowChennai
3rd rowBangalore
4th rowPune
5th rowPune

Common Values

ValueCountFrequency (%)
Hyderabad 87
17.4%
Ahmedabad 79
15.8%
Chennai 74
14.8%
Mumbai 65
13.0%
Pune 63
12.6%
Delhi 62
12.4%
Bangalore 55
11.0%
(Missing) 15
 
3.0%

Length

2025-08-19T01:03:09.076741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:03:09.170055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hyderabad 87
17.9%
ahmedabad 79
16.3%
chennai 74
15.3%
mumbai 65
13.4%
pune 63
13.0%
delhi 62
12.8%
bangalore 55
11.3%

Most occurring characters

ValueCountFrequency (%)
a 581
16.8%
e 420
12.1%
d 332
9.6%
n 266
 
7.7%
b 231
 
6.7%
h 215
 
6.2%
i 201
 
5.8%
m 144
 
4.2%
r 142
 
4.1%
u 128
 
3.7%
Other values (11) 799
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 581
16.8%
e 420
12.1%
d 332
9.6%
n 266
 
7.7%
b 231
 
6.7%
h 215
 
6.2%
i 201
 
5.8%
m 144
 
4.2%
r 142
 
4.1%
u 128
 
3.7%
Other values (11) 799
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 581
16.8%
e 420
12.1%
d 332
9.6%
n 266
 
7.7%
b 231
 
6.7%
h 215
 
6.2%
i 201
 
5.8%
m 144
 
4.2%
r 142
 
4.1%
u 128
 
3.7%
Other values (11) 799
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 581
16.8%
e 420
12.1%
d 332
9.6%
n 266
 
7.7%
b 231
 
6.7%
h 215
 
6.2%
i 201
 
5.8%
m 144
 
4.2%
r 142
 
4.1%
u 128
 
3.7%
Other values (11) 799
23.1%

product_name
Text

Missing 

Distinct485
Distinct (%)100.0%
Missing15
Missing (%)3.0%
Memory size48.1 KiB
2025-08-19T01:03:09.373769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length47
Mean length43.253608
Min length34

Characters and Unicode

Total characters20978
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique485 ?
Unique (%)100.0%

Sample

1st rowVoltas Table Top Dishwasher (Model_N299)
2nd rowVoltas Top Load Washing Machine (Model_K244)
3rd rowVoltas Inverter AC Air Conditioner (Model_X75)
4th rowVoltas Window AC Air Conditioner (Model_X101)
5th rowVoltas Split AC Air Conditioner (Model_Y128)
ValueCountFrequency (%)
voltas 476
 
18.1%
air 148
 
5.6%
top 122
 
4.6%
refrigerator 85
 
3.2%
water 83
 
3.2%
dispenser 81
 
3.1%
loading 81
 
3.1%
cooler 78
 
3.0%
dishwasher 77
 
2.9%
machine 75
 
2.9%
Other values (553) 1323
50.3%
2025-08-19T01:03:09.681546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2144
 
10.2%
o 1990
 
9.5%
e 1592
 
7.6%
l 1287
 
6.1%
a 1127
 
5.4%
r 1048
 
5.0%
i 971
 
4.6%
s 955
 
4.6%
t 930
 
4.4%
d 803
 
3.8%
Other values (53) 8131
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2144
 
10.2%
o 1990
 
9.5%
e 1592
 
7.6%
l 1287
 
6.1%
a 1127
 
5.4%
r 1048
 
5.0%
i 971
 
4.6%
s 955
 
4.6%
t 930
 
4.4%
d 803
 
3.8%
Other values (53) 8131
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2144
 
10.2%
o 1990
 
9.5%
e 1592
 
7.6%
l 1287
 
6.1%
a 1127
 
5.4%
r 1048
 
5.0%
i 971
 
4.6%
s 955
 
4.6%
t 930
 
4.4%
d 803
 
3.8%
Other values (53) 8131
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2144
 
10.2%
o 1990
 
9.5%
e 1592
 
7.6%
l 1287
 
6.1%
a 1127
 
5.4%
r 1048
 
5.0%
i 971
 
4.6%
s 955
 
4.6%
t 930
 
4.4%
d 803
 
3.8%
Other values (53) 8131
38.8%
Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
2025-08-19T01:03:09.967808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length13.5
Mean length10.726
Min length5

Characters and Unicode

Total characters5363
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique494 ?
Unique (%)98.8%

Sample

1st rowarjun_20
2nd rowsanjay219
3rd rownisha_buyer0
4th rowpreeti_deals8
5th rowswati.buyer01
ValueCountFrequency (%)
ajay.3 2
 
0.4%
rohit_9 2
 
0.4%
amit.6 2
 
0.4%
sanjay219 1
 
0.2%
ajaydeals2 1
 
0.2%
sanjay.zone8 1
 
0.2%
komal.450 1
 
0.2%
preeti.buyer7 1
 
0.2%
vinod.4 1
 
0.2%
divya_user920 1
 
0.2%
Other values (487) 487
97.4%
2025-08-19T01:03:10.399391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Interactions

2025-08-19T01:02:59.777385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:58.400491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:58.808275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:59.237717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:03:00.133061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:58.501178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:58.911020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:59.338518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:03:00.275701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:58.619787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:59.015018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:59.450846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:03:00.423883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:58.717213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:59.129392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:02:59.583782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-19T01:03:10.522238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
availabilitycapacity_kgcapacity_literscapacity_place_settingscapacity_tonscitycolorcomplaint_textcustomer_ratingdiscount_offeredenergy_rating_starsfeature_1platformprice_inrproduct_categoryresolved_statusreturn_statusreview_sentimentreviewer_locationsub_typetechnologywarranty_duration_monthswarranty_years
availability1.0000.1180.0000.0000.0000.0950.0000.0000.0000.0000.0390.0950.0540.0000.0000.0000.0000.0000.0340.0000.0000.0000.000
capacity_kg0.1181.0000.0000.0000.0000.1420.1210.1350.0140.1350.0000.0000.1490.0521.0000.0000.1460.0900.1970.1810.1740.2560.267
capacity_liters0.0000.0001.0000.0000.0000.0640.0000.000-0.069-0.0560.0000.4410.0000.0050.7820.1230.0540.0820.0850.4520.6400.1120.113
capacity_place_settings0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0860.0000.1480.1960.0001.0000.0000.0000.0560.0000.1041.0000.0000.000
capacity_tons0.0000.0000.0000.0001.0000.1240.1550.0000.0000.0000.0000.0000.0000.0001.0000.0330.0000.0000.0000.0360.0000.0000.000
city0.0950.1420.0640.0000.1241.0000.0840.0140.0550.0470.0880.0000.0000.0540.0000.0750.0680.0340.0410.0000.0400.0000.000
color0.0000.1210.0000.0000.1550.0841.0000.0540.0000.0890.0000.0000.0180.0000.0000.0620.0000.0000.0450.0340.0000.0000.022
complaint_text0.0000.1350.0000.0000.0000.0140.0541.0000.0000.0350.0000.0350.0730.0540.0000.0680.0000.0000.0440.0000.0000.0000.000
customer_rating0.0000.014-0.0690.0000.0000.0550.0000.0001.0000.0250.0810.0000.083-0.0050.0110.0000.0000.0000.0510.0000.0000.0000.000
discount_offered0.0000.135-0.0560.0860.0000.0470.0890.0350.0251.0000.0780.0000.0790.0150.0000.0310.1010.0120.0700.0000.0000.0000.000
energy_rating_stars0.0390.0000.0000.0000.0000.0880.0000.0000.0810.0781.0000.0000.0000.0000.0000.0620.0670.0000.0000.0240.0850.0000.000
feature_10.0950.0000.4410.1480.0000.0000.0000.0350.0000.0000.0001.0000.0000.1020.9470.1130.0000.0000.0710.5670.7450.0000.000
platform0.0540.1490.0000.1960.0000.0000.0180.0730.0830.0790.0000.0001.0000.0460.0000.1060.0000.0000.0000.0000.0750.0000.016
price_inr0.0000.0520.0050.0000.0000.0540.0000.054-0.0050.0150.0000.1020.0461.0000.0470.0840.0470.0000.0000.0610.0570.0000.000
product_category0.0001.0000.7821.0001.0000.0000.0000.0000.0110.0000.0000.9470.0000.0471.0000.1370.0000.0520.0000.9900.9970.0550.064
resolved_status0.0000.0000.1230.0000.0330.0750.0620.0680.0000.0310.0620.1130.1060.0840.1371.0000.0000.0000.0000.1130.1220.0750.072
return_status0.0000.1460.0540.0000.0000.0680.0000.0000.0000.1010.0670.0000.0000.0470.0000.0001.0000.0000.0430.0280.0000.0000.000
review_sentiment0.0000.0900.0820.0560.0000.0340.0000.0000.0000.0120.0000.0000.0000.0000.0520.0000.0001.0000.0000.0000.0700.0810.090
reviewer_location0.0340.1970.0850.0000.0000.0410.0450.0440.0510.0700.0000.0710.0000.0000.0000.0000.0430.0001.0000.1160.0000.0000.000
sub_type0.0000.1810.4520.1040.0360.0000.0340.0000.0000.0000.0240.5670.0000.0610.9900.1130.0280.0000.1161.0000.8580.0110.023
technology0.0000.1740.6401.0000.0000.0400.0000.0000.0000.0000.0850.7450.0750.0570.9970.1220.0000.0700.0000.8581.0000.0490.058
warranty_duration_months0.0000.2560.1120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0750.0000.0810.0000.0110.0491.0001.000
warranty_years0.0000.2670.1130.0000.0000.0000.0220.0000.0000.0000.0000.0000.0160.0000.0640.0720.0000.0900.0000.0230.0581.0001.000

Missing values

2025-08-19T01:03:00.710912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-19T01:03:01.058359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-19T01:03:01.771642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_idproduct_categorysub_typemodel_namecapacity_tonscapacity_literscapacity_kgcapacity_place_settingstechnologyfeature_1energy_rating_starscolorprice_inrmanufacturing_datewarranty_yearscustomer_ratingcityplatformdiscount_offeredavailabilitywarranty_duration_monthsreview_sentimentreturn_statuscomplaint_textresolved_statusreview_datereviewer_locationproduct_nameusername
0PID433097DishwasherTable TopModel_N299NaNNaNNaNNaNElectronic ControlProSmart Inverter Motor3.0Blue39733.002-03-202310.03.9DelhiAmazon5.0Out of Stock120.0PositiveNot ReturnedDelivered with damaged packaging.Resolved28-08-2023DelhiVoltas Table Top Dishwasher (Model_N299)arjun_20
1PID251899Washing MachineTop LoadModel_K244NaNNaN6.5NaNFully-AutomaticProSmart Inverter Motor3.0Silver56336.021-07-202210.04.2DelhiAmazon25.0Out of Stock120.0PositiveReturnedCooling issue after 2 months.In Progress14-01-2024ChennaiVoltas Top Load Washing Machine (Model_K244)sanjay219
2PID857398Air ConditionerInverter ACModel_X752.0NaNNaNNaNInverterTurbo Mode1.0Blue52924.024-02-20221.04.8BangaloreFlipkart20.0In Stock12.0PositiveNot ReturnedDelivered with damaged packaging.Unresolved20-07-2023BangaloreVoltas Inverter AC Air Conditioner (Model_X75)nisha_buyer0
3PID934528Air ConditionerWindow ACModel_X1011.0NaNNaNNaNNon-InverterSelf Diagnosis3.0Grey19256.030-05-20242.04.4HyderabadVijay Sales10.0Out of Stock24.0NeutralNot ReturnedNoisy operation at night.Resolved13-01-2023PuneVoltas Window AC Air Conditioner (Model_X101)preeti_deals8
4PID952837Air ConditionerSplit ACModel_Y1281.0NaNNaNNaNNon-InverterTurbo Mode3.0Black44924.017-03-2024NaN3.3BangaloreAmazon5.0In Stock120.0PositiveReturnedExcellent performance.Resolved18-10-2023PuneVoltas Split AC Air Conditioner (Model_Y128)swati.buyer01
5PID552702RefrigeratorDouble DoorModel_U332NaN450.0NaNNaNDirect CoolActive Fresh Blue Light1.0Grey29024.001-08-202210.03.1KolkataFlipkart30.0In Stock120.0NeutralNot ReturnedExcellent performance.Resolved25-10-2023ChennaiVoltas Double Door Refrigerator (Model_U332)sanjay_reviews74
6PID509633Water DispenserBottom LoadingModel_W126NaN15.0NaNNaNCompressor CoolingHot, Normal & Cold5.0White80497.0NaN2.04.1PuneCroma25.0In Stock24.0NegativeNot ReturnedDelivered with damaged packaging.Resolved20-07-2022DelhiVoltas Bottom Loading Water Dispenser M(odel_W126)ajay_zone42
7PID430190Washing MachineTop LoadModel_B495NaNNaN6.5NaNSemi-AutomaticGentleWave DrumNaNGrey13928.020-06-202310.04.5BangaloreVijay Sales20.0Out of Stock120.0NegativeReturnedRemote stopped working.Resolved21-08-2022PuneVoltas Top LoadW ashing Machine (Model_B495)vinod9
8PID482549NaNSplit ACModel_K1661.0NaNNaNNaNNon-InverterTurbo Mode4.0Silver18262.005-12-20235.03.4BangaloreAmazon5.0Out of Stock60.0NeutralReturnedNo issues so far.Unresolved09-11-2022MumbaiNaNradha_151
9PID164915Water DispenserBottom LoadingModel_Y440NaN20.0NaNNaNCompressor CoolingHot & Cold5.0Black47034.025-08-20235.04.9BangaloreFlipkart20.0Out of Stock60.0NeutralNot ReturnedSlow installation service.In Progress01-06-2024DelhiVoltas Bottom Loading Water Dispenser (Model_Y440)radhaindia70
product_idproduct_categorysub_typemodel_namecapacity_tonscapacity_literscapacity_kgcapacity_place_settingstechnologyfeature_1energy_rating_starscolorprice_inrmanufacturing_datewarranty_yearscustomer_ratingcityplatformdiscount_offeredavailabilitywarranty_duration_monthsreview_sentimentreturn_statuscomplaint_textresolved_statusreview_datereviewer_locationproduct_nameusername
490PID445336Air CoolerDesertModel_L193NaN50.0NaNNaNEvaporative CoolingRemote Control3.0Grey18654.013-05-20231.04.9HyderabadAmazon15.0Out of Stock12.0NaNReturnedDelivered with damaged packaging.In Progress12-10-2023PuneVoltas Desert Air Cooler (Model_L193)suresh.91
491PID108644Air ConditionerInverter ACModel_C21.0NaNNaNNaNInverterTurbo Mode4.0Silver55135.004-08-20235.05.0HyderabadAmazon25.0Out of Stock60.0PositiveNot ReturnedDelivered with damaged packaging.ResolvedNaNMumbaiVoltas Inverter AC Air Conditioner (Model_C2)ajay7
492PID878002RefrigeratorNaNModel_F317NaN250.0NaNNaNDirect CoolStoreFresh+1.0Black61806.031-01-20221.04.0PuneFlipkart0.0Out of Stock12.0NeutralReturnedRemote stopped working.In Progress09-07-2022HyderabadVoltas Single Door Refrigerator (Model_F317)deepa.zone5
493PID275253RefrigeratorSingle DoorModel_Z129NaNNaNNaNNaNFrost FreeStoreFresh+5.0Black56407.016-06-20242.03.1KolkataCroma15.0Out of Stock24.0NeutralNot ReturnedRemote stopped working.Resolved28-03-2022HyderabadVoltas Single Door Refrigerator (Model_Z129)vikasexpress2
494PID322665Air ConditionerSplit ACModel_B1311.0NaNNaNNaNNaNAdjustable Cooling3.0Grey67872.007-05-202310.04.1HyderabadCroma10.0Out of Stock120.0NegativeReturnedNo issues so far.Unresolved01-03-2022AhmedabadVoltas Split AC Air Conditioner (Model_B131)deepa85
495PID408384Air CoolerDesertModel_R303NaN90.0NaNNaNEvaporative CoolingIce Chamber5.0WhiteNaN03-03-202210.03.2ChennaiFlipkart10.0Out of Stock120.0PositiveReturnedNoisy operation at night.In Progress11-03-2023MumbaiVoltas Desert Air Cooler (Model_R303)seema.buyer22
496PID319590Washing MachineTop LoadModel_N221NaNNaN6.0NaNSemi-AutomaticProSmart Inverter Motor2.0Blue19647.023-06-20222.03.2ChennaiAmazon0.0Out of Stock24.0NegativeReturnedExcellent performance.In Progress09-11-2023HyderabadVoltas Top Load Washing Machine (Model_N221)shilpa767
497PID390184DishwasherFull SizeNaNNaNNaNNaN12.0Electronic ControlHygiene+5.0Grey14754.028-08-202210.04.5MumbaiVijay Sales20.0Out of Stock120.0PositiveNot ReturnedCooling issue after 2 months.In Progress30-06-2022HyderabadVoltas Full Size Dishwasher (Model_W230)rohitzone84
498PID966079Washing MachineFront LoadModel_S96NaNNaN6.0NaNFully-AutomaticProSmart Inverter Motor3.0White58325.019-01-20231.04.8PuneCroma30.0In Stock12.0NegativeReturnedExcellent performance.Resolved21-06-2022HyderabadVoltas Front Load Washing Machine (Model_S96)nisha_52
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